Using rotation measure grids to detect cosmological magnetic fields: A Bayesian approach

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Using rotation measure grids to detect cosmological magnetic fields: A Bayesian approach

Vacca, V. (author): Max Planck Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),Max Planck Society for the Advancement of Science (MPG),Istituto nazionale di astrofisica (INAF)

Oppermann, N. (author): Institut canadien d'astrophysique théorique,Canadian Institute for Theoretical Astrophysics

Enßlin, T.A. (author): Max Planck Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),Max Planck Society for the Advancement of Science (MPG),Ludwig-Maximilians-Universität München,Ludwig Maximilian University of Munich

Jasche, J. (author): Centre national de la recherche scientifique (CNRS),Ludwig-Maximilians-Universität München,Ludwig Maximilian University of Munich

Selig, M. (author): Ludwig-Maximilians-Universität München,Ludwig Maximilian University of Munich,Max Planck Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),Max Planck Society for the Advancement of Science (MPG)

Greiner, M. (author): Max Planck Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),Max Planck Society for the Advancement of Science (MPG),Ludwig-Maximilians-Universität München,Ludwig Maximilian University of Munich

Junklewitz, H. (author)

Reinecke, M. (author): Max Planck Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),Max Planck Society for the Advancement of Science (MPG)

Brüggen, M. (author): Universität Hamburg,University of Hamburg

Carretti, E. (author): Istituto nazionale di astrofisica (INAF)

Ferrari, C. (author): Istituto di Radioastronomia

Ferrari, L (author): Centre national de la recherche scientifique (CNRS)

Scaife, A. (author): National Radio Astronomy Observatory

Horellou, Cathy, 1967 (author): Chalmers tekniska högskola,Chalmers University of Technology

Ideguchi, S. (author): Ulsan National Institute of Science and Technology (UNIST)

Johnston-Hollitt, M. (author): Victoria University of Wellington

Pizzo, R. F. (author): Netherlands Institute for Radio Astronomy (ASTRON)

Rottgering, H.J.A. (author): Universiteit Leiden (UL),Leiden University (UL)

Shimwell, T. W. (author): Universiteit Leiden (UL),Leiden University (UL)

Takahashi, K. (author): Kumamoto University

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Max Planck Gesellschaft zur Förderung der Wissenschaften eV. (MPG) Istituto nazionale di astrofisica (INAF) (creator_code:org_t)

2016-06-03
2016
English.
In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 591, s. Art. no. A13-

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Journal article (peer-reviewed)

Abstract Subject headings

Determining magnetic field properties in different environments of the cosmic large-scale structure as well as their evolution over redshift is a fundamental step toward uncovering the origin of cosmic magnetic fields. Radio observations permit the study of extragalactic magnetic fields via measurements of the Faraday depth of extragalactic radio sources. Our aim is to investigate how much different extragalactic environments contribute to the Faraday depth variance of these sources. We develop a Bayesian algorithm to distinguish statistically Faraday depth variance contributions intrinsic to the source from those due to the medium between the source and the observer. In our algorithm the Galactic foreground and measurement noise are taken into account as the uncertainty correlations of the Galactic model. Additionally, our algorithm allows for the investigation of possible redshift evolution of the extragalactic contribution. This work presents the derivation of the algorithm and tests performed on mock observations. Because cosmic magnetism is one of the key science projects of the new generation of radio interferometers, we have predicted the performance of our algorithm on mock data collected with these instruments. According to our tests, high-quality catalogs of a few thousands of sources should already enable us to investigate magnetic fields in the cosmic structure.

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Using rotation measure grids to detect cosmological magnetic fields: A Bayesian approach

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